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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
RNA-seq prediction with deep convolutional neural networks.
| Date | Stars |
|---|---|
| 2026-07-31 | 257 |
| 2026-08-02 | 258 |
| 2026-08-05 | 259 |
| 2026-08-06 | 259 |
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<!---[](https://github.com/calico/github-template-python-library/actions/workflows/release-new-version.yml)---> <!---[](https://github.com/calico/github-template-python-library/actions/workflows/run-tests-formatting.yml)---> <!---[](https://github.com/calico/github-template-python-library/actions/workflows/check-prettier-formatting.yml)---> <img src="borzoi_logo.png" width="200" /> # Borzoi - Predicting RNA-seq from DNA Sequence Code repository for Borzoi models, which are convolutional neural networks trained to predict RNA-seq coverage at 32bp resolution given 524kb input sequences. The model is described in the following bioRxiv preprint:<br/> [https://www.biorxiv.org/content/10.1101/2023.08.30.555582v1](https://www.biorxiv.org/content/10.1101/2023.08.30.555582v1). Borzoi was trained on a large set of RNA-seq experiments from ENCODE and GTEx, as well as re-processed versions of the original Enformer training data (including ChIP-seq and DNase data from ENCODE, ATAC-seq data from CATlas, and CAGE data from FANTOM5). Here is a list of trained-on experiments: [human](https://raw.githubusercontent.com/calico/borzoi/main/examples/targets_human.txt) / [mouse](https://raw.githubusercontent.com/calico/borzoi/main/examples/targets_mouse.txt). The repository contains example usage code (including jupyter notebooks for predicting and visualizing genetic variants) as well as links for downloading model weights, training data, QTL benchmark tasks, etc. Contact *drk (at) @calicolabs.com* or *jlinder (at) @calicolabs.com* for questions about the model or data. ## Installation Borzoi depends on the [baskerville repository](https://github.com/calico/baskerville.git), which can be installed by issuing the following commands: ```sh git clone https://github.com/calico/baskerville.git cd baskerville pip install -e . ``` Next, install the [borzoi repository](https://github.com/calico/borzoi.git) by issuing the following commands: ```sh git clone https://github.com/calico/borzoi.git cd borzoi pip install -e . ``` To train new models, the [westminster repository](https://github.com/calico/westminster.git) is also required and can be installed with these commands: ```sh git clone https://github.com/calico/westminster.git cd westminster pip install -e . ``` These repositories further depend on a number of python packages (which are automatically installed with borzoi). See **pyproject.toml** for a complete list. The most important version dependencies are: - Python == 3.10 - Tensorflow == 2.15.x (see [https://www.tensorflow.org/install/pip](https://www.tensorflow.org/install/pip)) *Note*: The example notebooks require jupyter, which can be installed with `pip install notebook`.<br/> A new conda environment can be created with `conda create -n borzoi_py310 python=3.10`.<br/> Some of the scripts in this repository start multi-process jobs and require [slurm](https://slurm.schedmd.com/). Finally, the code base relies on a number of environment variables. For convenience, these can be configured in the active conda environment with the 'env_vars.sh' script. First, open up 'env_vars.sh' in each repository folder and change the few lines of code at the top to your local paths. Then, issue these commands: ```sh cd borzoi conda activate borzoi_py310 ./env_vars.sh cd ../baskerville ./env_vars.sh cd ../westminster ./env_vars.sh ``` Alternatively, the environment variables can be set manually: ```sh export BORZOI_DIR=/home/<user_path>/borzoi export PATH=$BORZOI_DIR/src/scripts:$PATH export P
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:f478f2974e6128f2, llm:Repository description: 'RNA-seq prediction with deep convolutional neural networks.' Language: Python. No topics provided.
matched fp:f478f2974e6128f2, llm:Repository description: 'RNA-seq prediction with deep convolutional neural networks.' Language: Python. No topics provided.
matched fp:f478f2974e6128f2, llm:Repository description: 'RNA-seq prediction with deep convolutional neural networks.' Language: Python. No topics provided.